CSI Simulation Why Additive Noise Fails and How to Fix It

Jul 16, 2026

Channel State Information (CSI) is used for indoor localization, activity recognition, and respiration monitoring, but collecting labeled data for every scenario is impractical. CSI-based training often uses simulated signals by perturbing recorded channel estimates, typically via additive white Gaussian noise (AWGN), assuming a linear, gain-invariant receiver chain. Empirical testing with RF jamming on six commodity receivers across two environments shows this assumption breaks: automatic gain control compresses CSI multiplicatively, yielding amplitude distributions AWGN cannot reproduce. To close the sim-to-real fidelity gap, MQTC learns per-subcarrier transformations via quantile mapping, temporal filtering, and copula-based reordering, reducing amplitude error eight-fold and improving downstream detection performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

Abstract

I. INTRODUCTION

CSI Simulation: Why Additive Noise Fails and How to Fix It Aymen Bouferrouma , Ildi Allab , Vincent Lendersb , Valeria Loscria aInria Lille-Nord Europe, Lille, France bUniversity of Luxembourg, Luxembourg arXiv :2607 .0 1882v 1 [ cs .NI] 2 Jul 2026 Abstract—Channel State Information (CSI) has become a widely used wireless channel sensing modality for applications such as indoor localization, activity recognition, and respiration monitoring. Because collecting labeled data under every target condition is impractical, training CSI-based models often relies on simulated data produced by adding noise or perturbations to recorded channel estimates, most commonly additive white Gaussian noise (AWGN). This practice assumes that the receiver chain between the antenna and the channel estimator is linear and gain-invariant. We test this assumption empirically using RF jamming as a controlled perturbation on 6 commodity receivers across 2 indoor environments. The assumption does not hold. Automatic gain control compresses the channel estimate multiplicatively before digitization, producing amplitude distributions that no additive noise variance can reproduce. To close the resulting fidelity gap, we propose MQTC, a measurementcalibrated model that learns the per-subcarrier distribution transformation through quantile mapping, temporal filtering, and copula-based cross-subcarrier reordering. MQTC reduces amplitude error 8-fold and closes 89% of the aggregate fidelity gap across four complementary dimensions. The improvement transfers directly to downstream tasks, where 5 classifiers from different families trained on MQTC-simulated data recover 93% of real-data jamming detection performance, while AWGN-trained classifiers remain near random decision. Index Terms—Channel State Information, simulation validation, receiver chain, Wi-Fi sensing, sim-to-real transfer, data augmentation I. INTRODUCTION Channel State Information (CSI) captures the complexvalued frequency response that an orthogonal frequencydivision multiplexing (OFDM) receiver estimates on every decoded frame as part of channel equalization. Because these per-subcarrier estimates capture fine-grained amplitude and phase distortions imposed by the propagation environment, CSI has become a general-purpose sensing modality for indoor localization, human activity recognition, gesture detection, and respiration monitoring [1], [2] . CSI extraction is now supported across commodity Wi-Fi chipsets [3]–[5], enabling broad deployment for sensing research. Many of these applications depend on simulated or synthetically augmented CSI for model training. CrossSense generates synthetic CSI from a single measurement set to enable crosssite sensing [6] . Noise-based perturbation of recorded CSI is a standard augmentation strategy in gesture recognition [7], activity recognition [8], indoor localization [9], and 5G positioning [10] . These approaches inherit, explicitly or implicitly, the textbook additive white Gaussian noise (AWGN) model [11] . Interference is added to a clean signal in the complex domain, where the model is well founded for raw baseband signals. In-phase and quadrature (I/Q)-level tools such as JamRF [12] generate interference waveforms along the same lines. The critical assumption is that what holds for raw baseband signals also holds for CSI after receiver processing. This assumption has not been empirically tested. Generative models bypass the additive formulation by learning CSI distributions directly [13], [14], but require large training sets and have not been validated against controlled measurements. A recent study on Wi-Fi sensing augmentation concluded that no prior work had systematically explored radio data augmentation, and that existing approaches are ad-hoc [15] . Before a received signal becomes a channel estimate, it passes through automatic gain control (AGC), analog-to-digital conversion (ADC), a fast Fourier transform (FFT) stage,

FAQs

Why does additive white Gaussian noise (AWGN) often fail for CSI simulation?

AWGN assumes a linear, gain-invariant receiver chain, but automatic gain control (AGC) compresses the channel estimate multiplicatively before digitization. This changes the amplitude distribution in a way additive noise variance cannot reproduce.

How was the simulation assumption tested in the document?

The authors empirically tested it using RF jamming as a controlled perturbation on six commodity receivers across two indoor environments, comparing the resulting CSI-level behavior against additive-noise predictions.

What is MQTC, and how does it improve CSI simulation fidelity?

MQTC is a measurement-calibrated model that learns the per-subcarrier distribution transformation through quantile mapping, temporal filtering, and copula-based cross-subcarrier reordering. It reduces amplitude error eight-fold and closes most of the aggregate fidelity gap.

Jul 16, 2026

Upload by Emma Wilson